The book presents the state of the art in machine learning algorithms (artificial neural networks of different architectures, support vector machines, etc.) as applied to the classification and mapping of spatially distributed environmental data. Basic geostatistical algorithms are presented as well. New trends in machine learning and their application to spatial data are given, and real case studies based on environmental and pollution data are carried out. The book provides a CD-ROM with the Machine Learning Office software, including sample sets of data, that will allow both students and researchers to put the concepts rapidly to practice.

Table of Contents

(1) LEARNING FROM GEOSPATIAL DATA

Problems and important concepts of machine learningMachine learning algorithms for geospatial dataContents of the book. Software descriptionShort review of the literature